Dear Gui, Because you're wrapping the calls to summary() in try(), the errors there shouldn't stop your simulation. It probably makes sense to test whether the object returned by try() is of class "try-error", and possibly to specify the argument silent=TRUE, examining the error messages in your code.
I didn't follow what you're trying to do with mapply(), since there appears to be only one data argument (myRes). Regards, John -------------------------------- John Fox Senator William McMaster Professor of Social Statistics Department of Sociology McMaster University Hamilton, Ontario, Canada web: socserv.mcmaster.ca/jfox > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of Guilherme Wood > Sent: August-11-10 1:05 PM > To: r-help@r-project.org > Subject: [R] sem & psych > > > Dear R users, > > I am trying to simulate some multitrait-multimethod models using the packages > sem and psych but whatever I do to deal with models which do not converge I > always get stuck and get error messages such as these: > > "Error in summary.sem(M1) : coefficient covariances cannot be computed" > > "Error in solve.default(res$hessian) : System ist f|r den Rechner singuldr: > reziproke Konditionszahl = 8.61916e-17" > > I am aware that these models could not be computed but it is ok, itis part of > what I am trying to show with the simulation, that under specifications x the > models converge more easily than under specifications y. > > When models do not converge I just let R write a string with -1s andhave > expected the simulation to go on. > But it doesn't happen! > Instead of that the computations using mapply to fill matrix rows with the > results of the single simulations break up and the whole simulation stops. > > How could I bring R just to spring the undesired solutions and go on up to > the end? > > Best, > Gui > > # Simulation MTMM > > > myMaxN <- 1 # > myRep <- 1 # number of replications in each experimental cell > > traitLoads <- c(0.3, 0.5, 0.7) # loads of observed variables on trait > factors > traitCorrs <- c(0.0, 0.4, 0.7) # correlations between traits > methodLoads <- c(0.2, 0.3, 0.4) # loads of observed variables on method > factors > methdCorrs <- c(0.0, 0.2, 0.4) # correlations between methods > SampleSize <- 500 # Sample size > myMaxIter <- 500 # Maximal number of interactions in every > model estimation > > nCond <- length(traitLoads)* length(traitCorrs)* length(methodLoads)* > length(methdCorrs) > myRes <- as.numeric(gl(nCond, 1, myRep*nCond)) > > myloadTrait <- as.numeric(gl(length(traitLoads), 1, length(myRes))) > mycorrTrait <- as.numeric(gl(length(traitCorrs), length(traitLoads), > length(myRes))) > myloadMethd <- as.numeric(gl(length(methodLoads), length(traitLoads) * > length(traitCorrs), length(myRes))) > mycorrMethd <- as.numeric(gl(length(methdCorrs), length(traitLoads) * > length(traitCorrs) * length(methodLoads), length(myRes))) > > theTotalReplications <- myRes > > ##### ######## BIG FUNCTION ####### ##### > > sizeControlGroup <- function(theTotalReplications) { # Big function for > running the whole simulation > > traitLoad <- traitLoads[myloadTrait[theTotalReplications]] > traitCorr <- traitCorrs[mycorrTrait[theTotalReplications]] > methodLoad <- methodLoads[myloadMethd[theTotalReplications]] > methdCorr <- methdCorrs[mycorrMethd[theTotalReplications]] > > > fx = matrix(c( > rep(traitLoad, 4), rep(0, 16), rep(traitLoad, 4), rep(0, 16), > rep(traitLoad, 4), rep(0, 16), rep(traitLoad, 4), > rep(c(methodLoad, 0, 0, 0), 4), rep(c(0, methodLoad, 0, 0), 4), rep(c(0, > 0, methodLoad, 0), 4), rep(c(0, 0, 0, methodLoad), 4)), ncol = 8) > > Phi = matrix(c(1, traitCorr, traitCorr, traitCorr, rep(0,4), > traitCorr, 1, traitCorr, traitCorr, rep(0,4), > traitCorr, traitCorr, 1, traitCorr, rep(0,4), > traitCorr, traitCorr, traitCorr, 1, rep(0,4), > rep(0,4),1, methdCorr, methdCorr, methdCorr, > methdCorr, rep(0,4),1, methdCorr, methdCorr, > methdCorr, methdCorr, rep(0,4),1, methdCorr, > methdCorr, methdCorr, methdCorr, rep(0,4),1), ncol = 8) > > mmtm <- sim.structure(fx, Phi, n = SampleSize, raw=T) > correMatrix <- mmtm$r > colnames(correMatrix) <- c(paste("item", seq(1:16), sep = "")) > rownames(correMatrix) <- c(paste("item", seq(1:16), sep = "")) > > > corForModel = correMatrix # establishes the correlation matrix > corForModel for model estimation > > M1 <- try(sem(CTM1, corForModel, SampleSize, maxiter = myMaxIter), > silent = FALSE) # SEM model estimation) # tries to estimate the CT(M-1) model > M2 <- try(sem(CTCM, corForModel, SampleSize, maxiter = myMaxIter), > silent = FALSE) # SEM model estimation) # tries to estimate the CTCM model > > if(M1$convergence > 2){ > convergenceM1 <- c(0) > resultsM1 <- as.numeric(c(rep(-1, 12))) } else { # else needs > to be in the same line as the last command > myModlChiM1 <- try(summary(M1)) > convergenceM1 <- as.numeric(M1$convergence) > chiM1 <- as.numeric(myModlChiM1$chisq) > dfM1 <- as.numeric(myModlChiM1$df) > chiM0 <- as.numeric(myModlChiM1$chisqNull) > dfM0 <- as.numeric(myModlChiM1$dfNull) > GFIM1 <- as.numeric(myModlChiM1$GFI) > AGFIM1 <- as.numeric(myModlChiM1$AGFI) > RMSEAM1 <- as.numeric(myModlChiM1$RMSEA[1]) > CFIM1 <- as.numeric(myModlChiM1$CFI) > BICM1 <- as.numeric(myModlChiM1$BIC) > SRMRM1 <- as.numeric(myModlChiM1$SRMR) > iterM1 <- as.numeric(myModlChiM1$iterations) > resultsM1 <- as.numeric(c(convergenceM1, chiM1, dfM1, chiM0, dfM0, > GFIM1, AGFIM1, RMSEAM1, CFIM1, BICM1, SRMRM1, iterM1)) > } > > if(M2$convergence > 2){ > convergenceM2 <- c(0) > resultsM2 <- as.numeric(c(rep(-1, 12))) } else { # else needs > to be in the same line as the last command > myModlChiM2 <- try(summary(M2)) > convergenceM2 <- as.numeric(M2$convergence) > chiM2 <- as.numeric(myModlChiM2$chisq) > dfM2 <- as.numeric(myModlChiM2$df) > chiM0 <- as.numeric(myModlChiM2$chisqNull) > dfM0 <- as.numeric(myModlChiM2$dfNull) > GFIM2 <- as.numeric(myModlChiM2$GFI) > AGFIM2 <- as.numeric(myModlChiM2$AGFI) > RMSEAM2 <- as.numeric(myModlChiM2$RMSEA[1]) > CFIM2 <- as.numeric(myModlChiM2$CFI) > BICM2 <- as.numeric(myModlChiM2$BIC) > SRMRM2 <- as.numeric(myModlChiM2$SRMR) > iterM2 <- as.numeric(myModlChiM2$iterations) > resultsM2 <- as.numeric(c(convergenceM2, chiM2, dfM2, chiM0, dfM0, > GFIM2, AGFIM2, RMSEAM2, CFIM2, BICM2, SRMRM2, iterM2)) > } > > designparameters <- c(traitLoad, traitCorr, methodLoad, methdCorr) > myResults <- c(designparameters, SampleSize, resultsM1, resultsM2) #, > convergenceM3, resultsM3) # > > return(myResults) > > } # End of function sizeControlGroup > > > ############ Loop for replications > ########################################################## > > totalRepeats = 100 > for(myRepeats in 1:totalRepeats){ > myTests <- mapply(sizeControlGroup, myRes, SIMPLIFY = F) # > effectSize > myTests <- matrix(unlist(myTests), nc=length(myTests[[1]]), byrow=T) > colnames(myTests) <- c("trL", "trCorr", "mthL", "methCorr", "n", "convM1", > "ChiM1", "dfM1", "Chim0", "dfm0", "GFIM1", "AGFIM1", "RMSEAM1", "CFIM1", > "BICM1", "SRMRM1", "iterM1","ConvM2", "ChiM2", "dfM2", "Chim0", "dfm0", > "GFIM2", "AGFIM2", "RMSEAM2", "CFIM2", "BICM2", "SRMRM2", "iterM2") > > write.table(myTests, paste("C:\\Dokumente und Einstellungen\\wood\\Eigene > Dateien\\Wood\\papers\\simulations\\rep", myRepeats, sep=""), sep = " ", > row.names = F) > > } > > > > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
